Along with the rapid development of Internet applications, real applications that perform service interactions via the Internet are becoming more popular. For example, in e-commerce, service interaction is equivalent to transaction associated with a commodity, and interacting parties of the service interaction are a providing party and a receiving party associated with the commodity, which may also be referred to as a seller and a buyer.
However, in existing environments network service interaction, especially on consumer to consumer (C2C) platforms, some service interaction providers may provide interaction content having a poor quality, which disagrees with the interaction content advertised by the providers to achieve the objective of deceiving receiving parties of the service interaction. Furthermore, the receiving parties do not have a better way to protect their rights after such service interactions are completed. For example, in e-commerce, a large number of small-scaled sellers with complicated sources of merchandise exist, and the difficulty of monitoring thereof is high. Some unscrupulous sellers may sell fake or shoddy goods to entrap buyers. If the goods have problems, the buyers find it difficult to protect their rights and cumbersome to provide evidence therefor, thus hurting the confidence of the buyers on online shopping and affecting the positive development of online transaction information industries.
Therefore, in order to effectively supervise network service interactions and restrict the providers from deceiving the receiving parties, after a service interaction is completed, a receiving party of the service interaction may evaluate a providing party. An evaluation result may be a positive evaluation such as a good comment, or a negative evaluation such as a bad comment. These evaluations may help receiving parties to discern providing parties, for example, identifying a seller providing fake goods.
Moreover, a service interaction platform may also perform identification for a user feature of a providing party based on evaluations given to the providing party in service interactions, such as determining whether the provider is providing interaction content with poor quality, and triggering a warning to facilitate manual intervention. Details thereof may include the following:
A method may include: for a providing party, counting the number of negative evaluations given to the providing party in service interactions, and when the number is greater than a preset number threshold, indicating that a user feature of the providing party is a negative feature, e.g., in e-commerce, which may further indicate that a seller is selling fake goods.
Another method may include: for a providing party, calculating a ratio of negative evaluations given to the providing party in service interactions with respect to all evaluations, and when the ratio is greater than a preset ratio threshold, indicating that a user feature of the providing party is a negative feature.
However, in real applications that perform service interactions based on the Internet, some receiving parties may give negative evaluations maliciously to providing party in the service interactions, and threaten the providing party thereby. In other words, some evaluations in the service interactions are not true and objective. Therefore, in the above solution, determining a user feature of a providing party merely based on the number of negative evaluations or the ratio of negative evaluations with respect to all evaluations of the providing party may be inaccurate.